Quick simulation of ATM buffers with on-off multiclass Markov fluid sources
ACM Transactions on Modeling and Computer Simulation (TOMACS)
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Fast simulation of rare events in queueing and reliability models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
SIGCOMM '95 Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
What are the implications of long-range dependence for VBR-video traffic engineering?
IEEE/ACM Transactions on Networking (TON)
Conference proceedings on Applications, technologies, architectures, and protocols for computer communications
On the relevance of long-range dependence in network traffic
IEEE/ACM Transactions on Networking (TON)
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue: Rare event simulation
Large Deviations for Small Buffers: An Insensitivity Result
Queueing Systems: Theory and Applications
Large deviations approximation for fluid queues fed by a large number of on/off sources
IEEE Journal on Selected Areas in Communications
Fast simulation of overflow probabilities in a queue with Gaussian input
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Fitting mixture importance sampling distributions via improved cross-entropy
Proceedings of the Winter Simulation Conference
| Hi-index | 0.00 |
We consider a queue fed by a large number, say n, on–off sources with generally distributed on- and off-times. The queueing resources are scaled by n: The buffer is B ≡ nb and the link rate is C ≡ nc. The model is versatile. It allows one to model both long-range-dependent traffic (by using heavy-tailed on-periods) and short-range-dependent traffic (by using light-tailed on-periods). A crucial performance metric in this model is the steady state buffer overflow probability.This probability decays exponentially in n. Therefore, if n grows large, naive simulation is too time-consuming and fast simulation techniques have to be used. Due to the exponential decay (in n), importance sampling with an exponential change of measure goes through, irrespective of the on-times being heavy or light tailed. An asymptotically optimal change of measure is found by using large deviations arguments. Notably, the change of measure is not constant during the simulation run, which is different from many other studies (usually relying on large buffer asymptotics).Numerical examples show that our procedure improves considerably over naive simulation. We present accelerations, we discuss the influence of the shape of the distributions on the overflow probability, and we describe the limitations of our technique.